25 KiB
Compatibility Matrix
This page summarizes common model + optimization combinations.
For the canonical, code-level list of model IDs recognized by
VideoGenerator.from_pretrained(...), see the registrations in
fastvideo/registry.py (register_configs(...) entries).
:::note
The full STA integration in fastvideo/ is archived from main and kept
in sta_do_not_delete:
https://github.com/hao-ai-lab/FastVideo/tree/sta_do_not_delete
We do this because we believe VSA is strictly better than STA for the
actively maintained main inference path.
:::
Registered Model IDs
Every Hugging Face model ID registered in fastvideo/registry.py on main
(commit 8d89f30d), grouped by family. Any ID below can
be passed to VideoGenerator.from_pretrained(...); FastVideo resolves the
matching pipeline and sampling defaults. The Family column is a
documentation grouping: it follows each registration's declared model_family,
except black-forest-labs/FLUX.1-dev, which declares none and is listed under
flux for readability. The Workloads column shows each
registration's declared workload_types; — means the entry is registered
without a UI workload option but is still loadable by ID. The Example
column links a runnable script in examples/inference/basic/ where one exists.
| Family | HuggingFace Model ID | Workloads | Example |
|---|---|---|---|
| cosmos | nvidia/Cosmos-Predict2-2B-Video2World |
T2V | — |
| cosmos25 | KyleShao/Cosmos-Predict2.5-2B-Diffusers |
T2V | basic_cosmos2_5_t2w.py |
| cosmos25 | nvidia/Cosmos-Predict2.5-14B |
T2V | basic_cosmos2_5_t2w.py |
| flux | black-forest-labs/FLUX.1-dev |
T2I | basic_flux_dev.py |
| flux2 | black-forest-labs/FLUX.2-klein-4Bblack-forest-labs/FLUX.2-klein-9B |
T2I | basic_flux2_klein.py |
| flux2 | black-forest-labs/FLUX.2-dev |
T2I | basic_flux2.py |
| gen3c | FastVideo/GEN3C-Cosmos-7B-Diffusers |
T2V | basic_gen3c.py |
| hunyuan | hunyuanvideo-community/HunyuanVideo |
T2V | — |
| hunyuan | FastVideo/FastHunyuan-diffusers |
T2V | — |
| hunyuan15 | hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v |
T2V | basic_hy15.py |
| hunyuan15 | hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_i2v_step_distilled |
I2V | basic_hy15.py |
| hunyuan15 | hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_t2v |
T2V | basic_hy15.py |
| hunyuan15 | hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_i2v_distilled |
I2V | basic_hy15.py |
| hunyuan15 | weizhou03/HunyuanVideo-1.5-Diffusers-1080pweizhou03/HunyuanVideo-1.5-Diffusers-1080p-2SR |
— | basic_hy15_1080p.py |
| hyworld | FastVideo/HY-WorldPlay-Bidirectional-Diffusers |
— | basic_hyworld.py |
| kandinsky5 | kandinskylab/Kandinsky-5.0-T2V-Lite-sft-5s-Diffusers |
T2V | basic_kandinsky5_t2v.py |
| kandinsky5 | kandinskylab/Kandinsky-5.0-T2V-Pro-sft-5s-Diffusers |
T2V | basic_kandinsky5_t2v.py |
| kandinsky5 | kandinskylab/Kandinsky-5.0-T2V-Lite-distilled16steps-5s-Diffusers |
T2V | basic_kandinsky5_t2v.py |
| kandinsky5 | kandinskylab/Kandinsky-5.0-T2V-Pro-distilled-5s-Diffusers |
T2V | basic_kandinsky5_t2v.py |
| kandinsky5 | kandinskylab/Kandinsky-5.0-I2V-Lite-5s-Diffusers |
I2V | basic_kandinsky5_i2v.py |
| kandinsky5 | kandinskylab/Kandinsky-5.0-I2V-Pro-sft-5s-Diffusers |
I2V | basic_kandinsky5_i2v.py |
| kandinsky5 | kandinskylab/Kandinsky-5.0-I2V-Pro-distilled-5s-Diffusers |
I2V | basic_kandinsky5_i2v.py |
| kandinsky6 | kandinskylab/Kandinsky-6.0-Pro-5s-Diffuserskandinskylab/Kandinsky-6.0-Pro-sft-5s-Diffuserskandinskylab/Kandinsky-6.0-Lite-5s-Diffusers |
T2V, I2V | basic_kandinsky6_ti2va.py |
| kandinsky6 | kandinskylab/Kandinsky-6.0-Pro-distill-5s-Diffuserskandinskylab/Kandinsky-6.0-Lite-distill-5s-Diffusers |
T2V, I2V | basic_kandinsky6_ti2va.py |
| kandinsky6_sr | kandinskylab/Kandinsky-6.0-VSR-5s-Diffuserskandinskylab/Kandinsky-6.0-VSR-distilled2steps-5s-Diffusers |
— | basic_kandinsky6_sr.py |
| lingbot_video | FastVideo/LingBot-Video-MoE-30B-A3B-Diffusers |
T2V | basic_lingbot_video.py |
| lingbot_video | FastVideo/LingBot-Video-Dense-1.3B-Diffusers |
T2V | basic_lingbot_video.py |
| lingbotworld | FastVideo/LingBot-World-Base-Cam-Diffusers |
I2V | basic_lingbotworld_base_cam.py |
| lingbotworld2 | robbyant/lingbot-world-v2-14b-causal-fast |
I2V | basic_lingbotworld2_causal_fast.py |
| lingbotworld_fast | FastVideo/LingBot-World-Fast-Diffusers |
I2V | basic_lingbotworld_fast.py |
| ltx2 | FastVideo/LTX2-Distilled-DiffusersFastVideo/LTX2.3-Distilled-DiffusersFastVideo/LTX-2.3-Distilled-Diffusers |
T2V | basic_ltx2_distilled.py |
| ltx2 | FastVideo/LTX-2.5-Distilled-Diffusers |
T2V, I2V | T2AV I2AV |
| ltx2 | FastVideo/LTX-2.5-Dev-Diffusers |
T2V, I2V | T2AV I2AV |
| ltx2 | Lightricks/LTX-2.3FastVideo/LTX2.3-baseFastVideo/LTX2.3-Diffusers |
T2V | basic_ltx2.py |
| ltx2 | Lightricks/LTX-2FastVideo/LTX2-baseFastVideo/LTX2-Diffusers |
T2V | basic_ltx2.py |
| mmaudio | FastVideo/MMAudio-large-44k-v2-Diffusers |
V2A, T2A | basic_mmaudio.py |
| matrixgame | FastVideo/Matrix-Game-2.0-Base-Distilled-DiffusersFastVideo/Matrix-Game-2.0-GTA-Distilled-DiffusersFastVideo/Matrix-Game-2.0-TempleRun-Distilled-DiffusersFastVideo/Matrix-Game-2.0-Base-DiffusersFastVideo/Matrix-Game-2.0-GTA-DiffusersFastVideo/Matrix-Game-2.0-TempleRun-Diffusersmignonjia/mg_longtuning_distilled_zeldamignonjia/mg_sf_distilled_zelda_1k_stepsmignonjia/mg_sf_distilled_zeldamignonjia/mg_causal_zeldamignonjia/mg_bidirectional_zelda |
I2V | basic_matrixgame2.py |
| matrixgame | FastVideo/Matrix-Game-3.0-Base-Distilled-Diffusers |
I2V | basic_matrixgame3.py |
| minimax_h3 | MiniMaxAI/MiniMax-H3 |
T2V, I2V | T2VA FL2VA Ref2VA |
| sd35 | stabilityai/stable-diffusion-3.5-medium |
T2I | basic_sd35_t2i.py |
| stable_audio | FastVideo/stable-audio-open-1.0-Diffusers |
T2V | basic_stable_audio.py |
| stable_audio | FastVideo/stable-audio-open-small-Diffusers |
T2V | basic_stable_audio_small.py |
| wan | Wan-AI/Wan2.1-T2V-1.3B-Diffusers |
T2V | basic.py |
| wan | Wan-AI/Wan2.1-T2V-14B-DiffusersFastVideo/Wan2.1-VSA-T2V-14B-720P-Diffusers |
T2V | — |
| wan | Wan-AI/Wan2.1-I2V-14B-480P-Diffusers |
I2V | — |
| wan | Wan-AI/Wan2.1-I2V-14B-720P-Diffusers |
I2V | — |
| wan | Wan-AI/Wan2.2-Animate-14B-Diffusers |
I2V | basic_wan_animate.py |
| wan | weizhou03/Wan2.1-Fun-1.3B-InP-Diffusers |
I2V | — |
| wan | IRMChen/Wan2.1-Fun-1.3B-Control-Diffusers |
— | basic_wan2_2_Fun.py |
| wan | FastVideo/FastWan2.1-T2V-1.3B-DiffusersFastVideo/FastWan2.1-T2V-14B-480P-Diffusers |
T2V | basic_dmd.py |
| wan | Wan-AI/Wan2.2-TI2V-5B-Diffusers |
T2V, I2V | basic_wan2_2_ti2v.py |
| wan | FastVideo/FastWan2.2-TI2V-5B-FullAttn-DiffusersFastVideo/FastWan2.2-TI2V-5B-Diffusers |
T2V, I2V | basic_dmd.py |
| wan | decart-ai/Lucy-Edit-Devdecart-ai/Lucy-Edit-1.1-Dev |
— | basic_lucy_edit.py |
| wan | FastVideo/Wan2.2-S2V-14B-Diffusers |
I2V | basic_wan_s2v.py |
| wan | Wan-AI/Wan2.2-T2V-A14B-Diffusers |
T2V | basic_wan2_2.py |
| wan | Wan-AI/Wan2.2-I2V-A14B-Diffusers |
I2V | basic_wan2_2_i2v.py |
| wan | wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers |
T2V | basic_self_forcing_causal.py |
| wan | rand0nmr/SFWan2.2-T2V-A14B-Diffusers |
T2V | basic_self_forcing_causal_wan2_2_t2v.py |
| wan | FastVideo/SFWan2.2-I2V-A14B-Preview-Diffusers |
I2V | basic_self_forcing_causal_wan2_2_i2v.py |
Note (stable_audio): the Stable Audio Open pipelines generate audio
(StableAudioT2AConfig / StableAudioOpenSmallConfig); they are registered
under the generic T2V workload option in the registry.
Note (MMAudio): the registered Hugging Face model ID is reserved but not
yet public. Follow the MMAudio inference guide
to convert the official weights locally and set MMAUDIO_MODEL_PATH.
Note (Cosmos Predict2.5 distilled): the released 2B Text2World student is
supported through local checkpoint conversion, but no public converted model ID
is registered yet. Follow the validation guide in
tests/local_tests/cosmos25/README.md, then pass the converted directory to
basic_cosmos2_5_distilled_t2w.py --model.
The public DFD Video2World student is also supported through local DCP
conversion. It uses exactly one conditioning image, 4 steps, 81 frames at
704x1280 and 24 FPS. Use basic_cosmos2_5_dfd_i2w.py; a public converted model
ID is not registered yet.
Note (MiniMax H3): T2VA, FL2VA, and Ref2VA all generate video with stereo audio. Use the Ref2VA example when passing ordered image, video, or audio references. Distilled Ref2VA PDD students (eight transformer forwards, with reference videos as sparse VSA regions) run through basic_fasth3_omniref_pdd.py; see FastH3 distilled checkpoint schedules.
Note (Kandinsky 6): the kandinsky6 IDs (Pro 30.1B and Lite 3.2B, each base
and distilled) generate video with audio from text, optionally plus an image (see the T2IVA guide); the
kandinsky6_sr IDs upscale an existing video (see the
Video SR guide).
Note (LTX-2.5): T2V and I2V produce synchronized video and audio. The
official Lightricks/LTX-2.5 repository uses gated, split component files, so
it must first be converted to FastVideo's component layout. See the
LTX-2.5 inference guide.
Note (Wan-VACE): not currently supported — no VACE pipeline or registered
model ID exists on main
(#1435). The closest
supported path is the Wan2.1-Fun control pipeline
(IRMChen/Wan2.1-Fun-1.3B-Control-Diffusers).
The symbols used have the following meanings:
- ✅ = Full compatibility
- ❌ = No compatibility
- ⭕ = Does not apply to this model
Models x Optimization
The HuggingFace Model ID can be passed directly to
from_pretrained(). FastVideo then uses model-specific default settings for
pipeline initialization and sampling.
Registered models absent from this table have not been validated against these optimizations: absence means untested, not incompatible.
<style> /* Target tables in this section */ #models-x-optimization + p + table { display: block; overflow-x: auto; width: 100%; font-size: 0.85rem; } #models-x-optimization + p + table td, #models-x-optimization + p + table th { text-align: center; white-space: nowrap; padding: 0.5em; } /* First two columns can wrap */ #models-x-optimization + p + table td:nth-child(1), #models-x-optimization + p + table td:nth-child(2) { white-space: normal; min-width: 120px; } #models-x-optimization + p + table td:nth-child(2) code { font-size: 0.75rem; } </style>| Model Name | HuggingFace Model ID | Resolutions | TeaCache | Sliding Tile Attn (Legacy Branch) | Sage Attn | VSA |
|---|---|---|---|---|---|---|
| FastWan2.1 T2V 1.3B | FastVideo/FastWan2.1-T2V-1.3B-Diffusers |
480P | ⭕ | ⭕ | ⭕ | ✅ |
| FastWan2.2 TI2V 5B Full Attn | FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers |
720P | ⭕ | ⭕ | ⭕ | ✅ |
| Wan2.2 TI2V 5B | Wan-AI/Wan2.2-TI2V-5B-Diffusers |
720P | ⭕ | ⭕ | ✅ | ⭕ |
| Lucy Edit Dev 5B*** | decart-ai/Lucy-Edit-Dev |
480P | ⭕ | ⭕ | ⭕ | ⭕ |
| Wan2.2 S2V 14B**** | FastVideo/Wan2.2-S2V-14B-Diffusers |
480P | ⭕ | ⭕ | ⭕ | ⭕ |
| Wan2.2 T2V A14B | Wan-AI/Wan2.2-T2V-A14B-Diffusers |
480P 720P |
❌ | ❌ | ✅ | ⭕ |
| Wan2.2 I2V A14B | Wan-AI/Wan2.2-I2V-A14B-Diffusers |
480P 720P |
❌ | ❌ | ✅ | ⭕ |
| Wan2.2 Animate 14B | Wan-AI/Wan2.2-Animate-14B-Diffusers |
720P | ⭕ | ⭕ | ⭕ | ⭕ |
| HunyuanVideo | hunyuanvideo-community/HunyuanVideo |
720px1280p 544px960p |
❌ | ✅ | ✅ | ⭕ |
| FastHunyuan | FastVideo/FastHunyuan-diffusers |
720px1280p 544px960p |
❌ | ✅ | ✅ | ⭕ |
| Wan2.1 T2V 1.3B | Wan-AI/Wan2.1-T2V-1.3B-Diffusers |
480P | ✅ | ✅ | ✅ | ⭕ |
| Wan2.1 T2V 14B | Wan-AI/Wan2.1-T2V-14B-Diffusers |
480P, 720P | ✅ | ✅ | ✅ | ⭕ |
| Wan2.1 I2V 480P | Wan-AI/Wan2.1-I2V-14B-480P-Diffusers |
480P | ✅ | ✅ | ✅ | ⭕ |
| Wan2.1 I2V 720P | Wan-AI/Wan2.1-I2V-14B-720P-Diffusers |
720P | ✅ | ✅ | ✅ | ⭕ |
| Matrix Game 2.0 Base Distilled | FastVideo/Matrix-Game-2.0-Base-Distilled-Diffusers |
352x640 | ⭕ | ⭕ | ⭕ | ⭕ |
| Matrix Game 2.0 GTA Distilled | FastVideo/Matrix-Game-2.0-GTA-Distilled-Diffusers |
352x640 | ⭕ | ⭕ | ⭕ | ⭕ |
| Matrix Game 2.0 TempleRun Distilled | FastVideo/Matrix-Game-2.0-TempleRun-Distilled-Diffusers |
352x640 | ⭕ | ⭕ | ⭕ | ⭕ |
| Matrix Game 3.0 Base Distilled | FastVideo/Matrix-Game-3.0-Base-Distilled-Diffusers |
720x1280 | ⭕ | ⭕ | ⭕ | ⭕ |
| GEN3C Cosmos 7B | FastVideo/GEN3C-Cosmos-7B-Diffusers |
704px1280p | ❌ | ❌ | ❌ | ⭕ |
Apple Silicon native runtime
| Release path | Model | Mode | Validated hardware | Status |
|---|---|---|---|---|
| MLX FastMetal T2V 1.3B | FastVideo/FastMetal-1.3B-QAD |
480x832, 81 frames, 3-step DMD, INT8 DiT + TAEHV decode | Apple M4 Max, 16 GB+ unified memory | Released |
| MLX FastMetal T2V 5B | FastVideo/FastMetal-5B-QAD |
480p / 720p, 81 frames, 3-step DMD, INT8 DiT + TAEHV decode; optional --fast, --fast-spatial, --refine. The checked-in example is T2V. CUDA Wan2.2 TI2V 5B is the image-capable path. |
Apple M4 Max, 16 GB+ unified memory | Released |
| MLX FastMetal T2V 14B | FastVideo/FastMetal-14B-QAD |
480p / 720p, 81 frames, 3-step DMD, INT8 DiT + TAEHV decode | Apple M4 Max, 36 GB+ unified memory | Released |
| MLX FastH3 V1 T2VA | FastVideo/FastVideo-Minimax-FastH3-Preview-v0.2 + locally converted DiT |
480p / 720p, 124 frames, 4-step DMD2, INT8/INT6/INT4 weight-only DiT, native video + audio VAE; optional temporal RIFE fast mode; optional spatial fast mode; optional VSA (tile 64/256, exempt/compete) on --include-vsa checkpoints |
Apple M4 Max, 36 GB unified memory | Source runtime; T2VA only |
| MLX FastH3 V2 T2VA | FastVideo/FastVideo-FastH3-8-Step-V2 + locally converted DiT |
480p / 720p, 124 frames, 8-step DMD2, INT8 weight-only DiT with --include-vsa, trained VSA 0.8 / tile 64, native video + audio VAE; AdaLN cache from fastvideo_inference.json |
Apple M4 Max, 36 GB unified memory | Source runtime; T2VA only |
| MLX FastH3 OmniRef Ref2VA | FastVideo/FastVideo-FastH3-Omni-8-Step-V1 + base MiniMax-H3 snapshot + locally converted transformer_ref |
Ordered image/video/audio references, 124 frames at 480p / 720p, the export's fused-block PDD ladder, INT8/INT6/INT4 weight-only DiT with --include-vsa, native video + audio VAE encoders for the references |
Apple M4 Max, 36 GB unified memory | Source runtime |
Apple Silicon uses the native MLX runtime. FastMetal-QAD is the packaged Wan
release. FastH3 V1 and FastH3 V2 use a source checkout and local DiT conversion.
CUDA FastWan-QAD (FastVideo/FastWan-QAD-1.3B,
FastVideo/FastWan-QAD-FP8-1.3B) is the NVIDIA release. See the
MLX install guide (and, for
reference-conditioned OmniRef, the
MLX OmniRef page) and the
FastMetal-QAD blog.
Note: Wan2.2 TI2V 5B has some quality issues when performing I2V generation. We are working on fixing this issue.
***Lucy Edit Dev uses a non-commercial model license. FastVideo support is focused on inference integration for video editing workflows.
****Wan2.2 S2V is audio-driven: it takes a reference image plus a speech/audio
track and animates the subject in sync with it. The official checkpoint ships
in native Wan format; scripts/checkpoint_conversion/wan_s2v_to_diffusers.py
repackages it into the Diffusers layout this repo id refers to (bundling the
wav2vec2-large-xlsr-53-english audio encoder). num_frames follows the
usual 4k+1 contract (default 81): one clip generates 84 frames and shows
81, and longer requests chain clips that carry the previous 73 frames forward
as motion context, as the official runner does. The source audio is muxed into
the saved MP4. Sequence parallelism is not yet wired up for this pipeline.
Sliding Tile Attn (Legacy Branch) entries refer to the archived
sta_do_not_delete branch workflow, not active main inference wiring.
Canonical Supported IDs
The authoritative source for model-ID recognition is
fastvideo/registry.py. If a model ID is registered there, FastVideo can
resolve default pipeline and sampling configuration for it.
Note (GEN3C): The official nvidia/GEN3C-Cosmos-7B repo provides a raw
model.pt checkpoint. Use a Diffusers-format repo (for example,
FastVideo/GEN3C-Cosmos-7B-Diffusers) or convert locally with
scripts/checkpoint_conversion/convert_gen3c_to_fastvideo.py.
FastH3 OmniRef on consumer devices
OmniRef (Ref2VA) runs the same NVFP4 stack on one consumer Blackwell device - RTX 5090, RTX PRO 6000 and DGX Spark - with a packed NVFP4 export, FP4 sparse attention with the dense prefix rows split off, and the pre-packed NVFP4 decoder. An RTX 4090 runs the closest sm_89 equivalent (FP8 linears, bf16 VSA). Per-device profiles, recipes and the environment are in FastH3 OmniRef inference.
There is no MLX Ref2VA path yet: the Apple Silicon rows above are T2V/FL2VA.
Hardware and OS
Per the installation guides:
- NVIDIA GPU (x86_64) — CUDA 12.6 or 13.0; see the GPU install guide.
- NVIDIA DGX Spark (GB10, aarch64) — CUDA 13, from-source kernel build; see the DGX Spark install guide. Two Sparks over QSFP use Ray sequence parallel; see Pair two NVIDIA DGX Sparks.
- Apple silicon — macOS 14 or newer; MLX runtime for FastMetal-QAD, FastH3
V1, and FastH3 V2. See the
MLX install guide. The older
basic_mps.pydemo is PyTorch MPS only.
Optimization-specific hardware constraints (e.g. STA requiring Hopper) are listed under Special requirements.
For the platform overview, including AMD ROCm and the distinction between RTX, data center CUDA GPUs, DGX Spark and MLX, see Hardware and runtimes.
Special requirements
Sliding Tile Attention
- Full STA pipeline usage is on the archived branch: https://github.com/hao-ai-lab/FastVideo/tree/sta_do_not_delete
- STA currently requires Hopper GPUs (H100s).
Matrix Game 2.0
- Image-to-video game world models with keyboard/mouse control input
- Three variants available: Base (universal), GTA, and TempleRun
- Each variant has different keyboard dimensions for control inputs